Neurophysiological central auditory processing evaluation system and method

ABSTRACT

A system and method of central auditory processing testing and evaluation provides for identifying clinically relevant neural synchrony in the auditory brainstem pathway. The system or method finds use as a tool to evaluate auditory processing disorders, and hence, potential auditory system and/or learning disabilities. The system or method may further find use in the selection and fitting of hearing corrective appliances such as hearing aid or cochlear implant devices and/or in the selection and implementation of auditory training regimens.

FEDERAL STATEMENT

The U.S. Government has a paid up license in this invention and theright in limited circumstances to require the patent owner to licenseothers on reasonable terms as provided for by the terms of Grant No.ROI-DC01510 awarded by the NIH.

RELATED APPLICATION

This invention was made with government support under grant number R01DC001510 awarded by the National Institutes of Health. The governmenthas certain rights in the invention.

TECHNICAL FIELD

This patent relates to central auditory processing testing, and moreparticularly, to a system and method incorporating neurophysiologicalevaluation of central auditory processing disorders and associatedhearing and learning disabilities.

BACKGROUND

Recording the brainstem's response to sound has long been established asa valid and reliable means to assess the integrity of the neuraltransmission of acoustic stimuli. Transient acoustic events induce apattern of voltage fluctuations in the brainstem resulting in a familiarwaveform that yields information about brainstem nuclei along theascending central auditory pathway. Accurate stimulus timing in theauditory brainstem is a hallmark of normal perception.

Abnormal perception, understanding and processing of spoken language arefundamental criteria in the diagnosis of many learning disabilities.Currently, central auditory processing disorders are diagnosed through acentral auditory processing (CAP) evaluation. Audiologists andspeech-language pathologists perform a series of tests, all of which areperceptual and/or audiological in nature, i.e. subjective- notphysiological or objective. Auditory brainstem response (ABR) testingprovides a physiological indication, but no connection has beenestablished between conventional ABR results and learning disabilities.

Children and adults diagnosed with learning disabilities exhibit highlyvariable subject profiles. Many factors can contribute to currentdiagnosis of a learning problem. These include variations in: basicperceptual physiology and higher levels of cognitive function andattention, experientially developed compensatory mechanisms, exposure toprevious remedial interventions and differing interpretations ofdiagnostic categories by clinicians. A consistent and reliable methodfor diagnosing individuals with learning disabilities has yet to beestablished.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustration of a central auditory processingevaluation system.

FIG. 2 is a graph depicting a stimulus signal and a resulting evokedbrainstem response.

DETAILED DESCRIPTION

This patent relates to a system and method of central auditoryprocessing testing and evaluation. In accordance with the hereindescribed embodiments, the system and method provide for identifyingclinically relevant neural synchrony in the auditory brainstem pathway.A system or method in accordance with the described embodiments findsuse as a tool to evaluate auditory processing disorders, and hence,potential auditory system and/or learning disabilities. The system ormethod may further find use in the selection and fitting of hearingcorrective appliances such as hearing aid or cochlear implant devicesand/or in the selection and implementation of auditory trainingregimens.

The system and method utilize a biological marker of auditory processing(or BioMAP), that reflects preconscious aggregate neural activity in theauditory brainstem pathway. The BioMAP exhibits both phasic and tonicencoding, mimicking the acoustic characteristics of the speech signalwith high fidelity allowing characterization of normal neural synchronyand subsequent individual evaluation for clinically relevantasynchronous responses. Individual results comparison to a database ofnormative responses, established from a normal population with highrepeatability and low variability, allows the BioMAP to serve as abiological marker for predicting hearing associated disabilitiesincluding early detection of childhood learning disabilities.

In one possible implementation, the system and corresponding method ofoperation of the system is adapted to present a short auditory stimulusto a test subject. Commercially available electrophysiologicalacquisition technology acquires response data from the subject'sbrainstem response to the stimulus. Evaluation of the response datausing various techniques including statistical analysis and comparisonto a database of normative results, provides an objective indication ofthe presence of central auditory processing disorders.

Referring to FIG. 1, a system 100 includes a processor or controller 102coupled to a transducer controller 104, a user interface 106 and adatabase 108. Coupled to the controller 102 via the transducercontroller 104 are an audio transducer 110 and a plurality of electrodes112. While shown as a single element, transducer controller 104 may beseparated into elements 104 a and 104 b. Element 104 a may deliver astimulus to the audio transducer 110, and element 104 b may receive andprocess brainwave signal information from the plurality of electrodes112. The transducer controller 104 is any suitable stimulus delivery anddata acquisition system, including PC-based stimulus delivery and dataacquisition systems such as those available from Bio-logic SystemsCorporation, Mundelein, Ill. or Compumedics, El Paso, Tex. The audiotransducer 110 may be an insert earphone such as the ER-3 insertearphone available from Etymotic research, Elk Grove, Ill. Theelectrodes 112 may be Ag—AgCl scalp electrodes, which may be positionedon the test subject from Cz (active) to ipsilateral earlobe (reference)with forehead ground.

The controller 102 may be a personal computer (PC) or other suitablegeneral purpose computing device, or may be a dedicated processor. Thecontroller 102 may include memory 114 within which instructions areretained directing the operation of the controller 102 for carrying outthe herein described methods and processes. That is, the controller 102responsive to a control program retained in the memory 114 operates togenerate a test stimulus signal, communicates the test stimulus signalto the transducer controller 104 for generation of an audio stimulusthat is presented to the test subject via the audio transducer 110. Thecontroller 102 is further operable to obtain brainstem response data viathe electrodes 112 and the transducer controller 104. The brainstemresponse data may be stored within the memory 114, written to thedatabase 116 or presented to a user of the system via the user interface106. The user interface 106 further permits the user to provide orselect instructions and/or parameters for the controller 102 forparticular test types, testing parameters and other required data forimplementing testing.

The database 116 in addition to including a data structure suitable forstorage of acquired brainstem response data, may include one or moredata structures used to stored data for analysis of the acquiredbrainstem response data. For example, the database 116 may contain oneor more data structures containing normative response data to which theacquired brainstem response data may be compared to provide comparisondata. The database 116 may further contain criteria data for evaluatingthe comparison data for determining the existence in the test subject ofa central processing disorder, auditory disability and/or learningdisability. The database 116 may still further contain data permittingthe recommendation of remedial measures, such as selection of hearingassistive appliances and/or auditory training regimens.

In one embodiment, a test group having a statistically significantmembership is used to develop the normative data retained in thedatabase 116. The members of this group are selected for having nomedical or learning difficulties. For example, before acceptance intothe test group each member must first meet minimum acceptable criteriaon accepted learning and achievement testing as well as on one or morehearing evaluation methodologies, such as, evoked auditory brainstemresponse (ABR) tests.

In one embodiment, the testing methodology includes developing astimulus signal that may be transduced to form an audio stimulus forcommunication to the test subject. The stimulus signal may include atransient peak element and a sustained element. Such a signal is typicalof speech, and for example, the stimulus signal may be a multi-formantsynthesized speech sound. In one exemplary embodiment, the stimulussignal is a five-formant synthesized Ida! having a 40 millisecond (ms)duration.

The audio stimulus may be presented as a single stimulus or as part of atrain of stimuli, monaurally or binaurally, with the same or alternatingpolarities, at constant or varying sound pressure level (SPL), atconstant or variable intervals, and in the presence or absence of noise.The criteria for the presentation of the audio stimulus are dependent onthe type of evaluation being performed and the type of disability beingscreened or identified. The /da/ stimulus may be presented in a stimulustrain, monaurally, in alternating polarities, at 80 dB SPL to the rightear via an insert earphone, with an inter-stimulus interval of 51 ms. Asound source is also provided to the non-test ear at less than 40 dBSPL. The stimulus train may consist of train segments wherein thestimuli within a segment have an inter-segment interval, measured inmilliseconds, and the train segments have an inter-train interval, alsomeasured in milliseconds. For example, the 40 ms /da/ stimulus may bepresented in segments of four stimuli separated by an inter-stimulusinterval, e.g., 10 ms, with each segment being separated by aninter-train interval, e.g., 30 ms.

Typically a large number of stimuli are presented and correspondingresults recorded and combined into an average waveform. One example ofcollecting and determining the average waveform may be recording theresults as a number of representative files, averaging the data, andplotting the data as an average waveform. The stimuli may be presentedas three sets of 1000 events in opposite polarities. For example, thestimulus may be presented a total of 6000 times, 3000 times each foreach polarity, with a cycle of 1000 stimulus events. The results may bepresented as a number of files of the recorded results as presented tothe subject in one polarity, each file including a complete stimuluscycle. For example, a test involving 6000 stimulus presentations mayresult in 6 separate data result files. The data of the resulting filesmay then be combined to achieve an average waveform. With reference toFIG. 2, an example of the resulting average waveform may be illustratedby 202.

The electrophysiological brainstem response to a speech sound may berepresented as a complex waveform 202. The brainstem response to the/da/ stimulus consists of a number of major peaks. The number of peaksconsidered may be decreased or increased without a loss of testaccuracy. For example, peaks may be examined from merely the waveform'sonset portion 204, the frequency following response (FFR) portion 206,or a combination of either or both portions. With reference to FIG. 2,any combination of peaks V, A, C and F may be reliable responsemeasurements. FIG. 2 illustrates the stimulus waveform 200 for thestimulus /da/ and the resulting average brainstem response waveform 202,averaged from brainstem response data obtained from thirty-eight testsubjects. The waveform 202 illustrates transient peaks making up theonset portion 204 as well as sustained elements making up the FFRportion 206. The onset of the speech stimulus /da/ response includes apositive peak (V) followed immediately by a negative trough (A).Following the onset 204 or transient response, peaks C and F are presentin the FFR portion 206. Additional peaks are also visible. It istherefore possible to determine, on a normative basis, the latency andamplitude of each peak for various stimuli, both in quiet and in noiseenvironments.

In addition to the latency and amplitude of the individual peaks, e.g.,V, A, C and F, relationships within the data may be determined andanalyzed. For example, inter-peak intervals, amplitude, slope and areaare measurable. Statistical relationships also may be established.Characteristics of the onset portion 204 (e.g., the VA inter-peak slopeand the VA inter-peak area) as well as characteristics of the FFRportion 206 (e.g., amplitude determined using root-mean-square (RMS),Fourier transform or other suitable special analysis techniques,stimulus-to-response correlation, and quiet-to-noise inter-responsecorrelation) may be measured.

All of these data when gathered from subjectively determined normal testsubjects are indicative of normal brainstem response to the subjectstimulus. Moreover, research demonstrates that speech-evoked brainstemresponse faithfully reflects many acoustic properties of the speechsignal. In normally-perceiving auditory systems, stimulus timing, on theorder of fractions of milliseconds, is accurately, precisely andrepeatably represented at the level of the brainstem. Thus, thesenormative data associated with normal response characteristics whenstored as part of the database 116 provide a tool for comparison ofbrainstem response data from a test subject as part of a hearing and/orlearning disability or more generally a central processing disorderevaluation system and method.

In operation of the system 100 as a tool for hearing and/or learningdisability evaluation, responsive to a user instruction received via theinterface 106, the controller 102 initiates a test as herein described.It should be noted that the system 100 may be capable of conducting anynumber of hearing tests, such as, evoked auditory brainstem response(ABR), otoacoustic emission (OAE) and the like in addition to the hereindescribed protocols. Thus, the system 100 may be a robust device for theevaluation and diagnosis of various hearing-related disorders.

The test may consist of presentation of an acoustic stimulus, e.g., the/da/ stimulus; acquiring brainstem response data from the test subjectresponsive to the acoustic stimulus; analyzing the brainstem responsedata to identify response characteristic data; comparing the responsecharacteristic data to a set of normative data to provide comparisondata; and determining an existence of a central processing disorderbased upon the comparison data.

In that regard, the control program retained in the memory 114, orotherwise provided to the controller 102, may include at least one dataprocessing routine. The control program may include three dataprocessing routines. The first routine achieves extraction from theresponse characteristic data from the brainstem response. That is, thefirst routine identifies the position of a number of the peaks (e.g.,peaks V, A, C and F) in the brainstem response data and recordscharacterizing information regarding the peaks. The characterizinginformation may include root mean square (RMS) analysis, Fast FourierTransform (FFT) analysis, and cross-correlation calculations. Theresults of these calculations may then be saved to the database 116 tobe compared and analyzed in routine two. One example of suitablealgorithms for these calculations may be supplied by MATLAB® as producedby The Mathworks, Inc. of Natick, Mass.

The test may optionally include an analysis of subject response in thepresence of background noise. The background noise may be in the form ofstimuli other than the /da/ that may cause a brainstem response. Withbackground noise, the data may be modified to allow more efficientextraction and identification of the peaks. For example, the data may gothrough a De-Noising Routine. One example of an effective De-NoisingRoutine may be based on Wavelet Decomposition or any other suitablemethod such as those described in “De-Noising by Wavelet Transform” byQian, “On wavelet analysis of auditory evoked potentials” by Bradley etal., or “Single-trial event-related potentials with wavelet de-noising”by Quiroga, et al.

For the first routine, characteristics of the onset portion 204 (e.g.,the VA inter-peak slope and the VA inter-peak area) as well as the FFRportion 206 (e.g., amplitude determined using root-mean-square (RMS), ameasurement of spectral content representation in the brainstem responseusing a Fast Fourier Transform or other suitable techniques,stimulus-to-response correlation, and quiet-to-noise interresponsecorrelation) may be measured.

In the onset portion 204, the VA inter-peak slope may be a measure ofthe onset timing, and may be defined as the change in amplitude withrespect time. One example of the VA inter-peak slope may be calculatedas follows:

Slope = (VA  complex  amplitude)(VA  complex  duration) = (peak  A  amp − peak  Vamp)  I  (peak  A  latency − peak  V  latency).

Also in the onset portion, the VA inter-peak area is a measure of theonset magnitude. One example of the VA inter-peak area may be calculatedas follows:

1) subtract wave A amplitude from the amplitude of every point betweenwave V and wave A, and

2) sum the resulting amplitudes of all points between peaks in V and A.

In the FFR portion 206, RMS is a measure of the magnitude and may becalculated as the square root oft he mean of the squares of theamplitude. One example of calculating the RMS may be as follows:

Step 1: Create a baseline FFR, normalized such that the mean is zero;

Step 2: Square the amplitudes of each point in the FFR;

Step 3: Sum the results of step 2;

Step 4: Divide the result of step 3 by the total number of selectedpoints in the FFR; and Step 5: Take the square root of the result ofstep 4.

A Fast Fourier Transform (FFT) may be used to obtain the spectralcomponents of the sustained portion of the response that correspond tothe stimulus fundamental frequency (Fo amplitude), the frequencies ofthe first formant of the stimulus (F1 amplitude), and the frequencies ofthe higher frequencies of the stimulus (HF). F0 amplitude, F1 amplitude,and HF amplitude may be measures of how well the spectral component ofthe stimulus is represented in the brain stem response. The baselinedFFR region may be windowed with a 2 ms on −2 ms off Hanning ramp. Toincrease the spectral estimate, the windowed FFR region may be paddedwith zeros (to the length n, where n is the number of samples in 1second) before taking the FFT. F0 amplitude may be calculated as theabsolute value of the FFT amplitude across the range of fundamentalfrequencies. F1 amplitude may be calculated as the average of theabsolute values of the FFT amplitudes across the first formantfrequencies. HF may be calculated as the average of the absolute valuesof the FFT amplitudes across the second and third formant frequencies.Fo amplitude, F1 amplitude, and HF amplitude values may also beevaluated with respect to the presence of those frequencies in thenon-stimulus-evoked neural activity.

Also in the FFR portion 206, the Stimulus-to-Response correlation may bea measure of how well the timing features of the stimulus waveform arepreserved in the response. The correlation may also be a measure of howwell the response phase locks to the stimulus. To account for the timeit takes for the acoustic stimulus to travel through the nervous system(approximately 7 to 11 ms), a static portion of the 2000 Hz low-passfiltered version of the stimulus that includes the harmonic segment maybe cross-correlated with a portion of the response. A series ofcrosscorrelations may be performed, with a lag between the stimulus andresponse increasing incrementally with each successive correlation. TwoPearson's correlation coefficients (r-value) may then be reported: 1)the maximum positive r-value in the 8 to 10 ms lag range and the maximumnegative r-value in the 7 to 9 ms lag range.

A Quiet-to-Noise Inter-Response Correlation may also be measured.Background noise may introduce a delay in the brainstem responsecompared to the response in quiet. The delay may be on the order of 0 to2 ms, and may be found by finding the maximum Pearson's correlationcoefficient in a series of correlations between a static portion of thequiet response corresponding to the FFR period, and an equivalently longportion of the noise file. With each successive correlation, the noiseresponse may be shifted later in time.

A second routine provides for clinical review of the characteristic datawith respect to the normative data stored in the database 116.Furthermore, complex associations within the data, such as inter-peakintervals, amplitude, slope and area as well as statisticalrelationships and characteristics of the FFR portion 206 may beconsidered. A score may be established based on the first routinecalculations that may contribute to the clinical review. The score maybe called a BioMAP score and consist of the normative values of five ABRmeasures including, in the onset portion 204: 1) wave V latency; 2) waveA latency; 3) VA slope; and in the FFR portion 206: 4) the averageenergy of the measured first formant frequencies (F1); and 5) theaverage energy of the high frequencies (HF).

For each of the five measures, a standard score or z-score for eachmeasure may be calculated. The quantity z represents the number ofstandard deviations between the raw score and the mean; it is negativewhen the raw score is below the mean, positive when above. The z-scoresmay allow easier comparison across the different measures. Further, eachz-score may be modified so that each may be easier to compare. Forexample, for each of the wave V latency, wave A latency, and VA slope,larger z-score values may be more abnormal. However, the opposite may betrue for the average energy of the measured first formant frequenciesand the average energy of the high frequencies. The negative z-scoresmay then be multiplied by −1 so that the z-scores may more easily becompared.

Each z-score may then be assigned a value depending on the z-scorevalue. For example, a z-score greater than 2 may be assigned a value of4, a z-score greater than 1.5, but less than 2 may be assigned a valueof2, a z-score greater than 1, but less than 1.5 may be assigned a valueof 1, and any z-score less than 1 may be assigned a value of 0. Theassigned values may then be added together to achieve an intermediatescore.

However, because the five measures were converted to standard scores, itmay be possible to achieve identical intermediate scores despite havingvery different measurements. For example, a subject having an abnormalmeasurement in the onset phase 204 may achieve an identical intermediatescore as another subject having an abnormal measurement in the FFR phase206 despite the fact that abnormal measurements in the onset phase 204are a stronger indication of a subject's abnormality than similarmeasurements during FFR 206. Therefore, the five measures may beanalyzed to determine which variables discriminate between normal andabnormal subjects. The differences between the five measures may betaken into account using a discriminant function to determine apredictor of group membership, or a group predictor (X). An example of adiscriminant function may be as follows:

X=(a×Wave V Latency)+(b×Wave A Latency)+(c×VA Slope)+(d×F1)+(e×HF)+kWhere a, b, c, d, and e are coefficient values that may define therelationship of each of the five measures, and k is a constant value.The constant value may be added to ensure that the group predictor willbe either above 0 or below 0. The coefficients and the constant may befound using analytical data mining software such as SPSS® as produced bySPSS, Inc. of Chicago, Ill. If the group predictor is below 0, thesubject is predicted to be abnormal and a value of 1 may be added to theintermediate score. If the group predictor is above 0, the subject ispredicted to be normal and a value of 1 may be subtracted from theintermediate score. The combination of the intermediate score and thegroup predictor may be a final composite score, or the BioMAP score.This may result in a range of possible values from 0 to 22.

The data resulting from scoring (i.e., the final composite score) maythen be processed by a third routine in a manner that allowsdetermination of presence of a central auditory processing disability,and particularly, a central auditory processing associated learningdisability. The determination may include comparison of the BioMAP scoreagainst a range of BioMAP scores indicating a delineation between normaland abnormal subjects. For example, a BioMAP score within a specificrange (i.e., 5 to 22) may indicate a central processing disorder in thesubject, while a lower score (i.e., 0 to 4) may indicate the absence ofa central processing disorder.

In the example described, the system 100 finds application in thedetermination of a central auditory processing disability, andparticularly an associated learning disability. The system 100 andmethods may find further application with the selection and fitting ofhearing assistive appliances such as hearing aids and/or cochlearimplants. That is, comparison of the characteristic data with normativedata allows for the identification of specific hearing relateddisorders. Selection of a hearing assistive appliance or alternativelyan auditory training regime is custom selectable for the individual.

The invention has been described in terms of several embodiments,including a number of features and functions. Not all features andfunctions are required for every embodiment of the invention. Thefeatures discussed herein are intended to be illustrative of thosefeatures that may be implemented; however, such features should not beconsidered exhaustive of all possible features that may be implementedin a device configured in accordance with the embodiments of theinvention. Moreover, the herein described embodiments are illustrativeand not limiting of the invention. The invention is defined and limitedonly by the following claims.

We claim:
 1. A system comprising: a controller coupled to a databaseincluding normative data, the controller including an associated memoryincluding a control program, having one or more data processing routinesconfigured to extract brainstem response data from a brainstem responseand store it in a database; a transducer controller coupled to thecontroller, the transducer controller operable to generate an acousticstimulus response to a stimulus signal received from the controller andto acquire brainstem response data from the brainstem response; atransducer coupled to the transducer controller to transduce theacoustic stimulus to provide an audible signal, and to present theaudible signal to the test subject; a plurality of electrodes configuredto detect the brainstem response within the test subject as a result ofthe presentation of the acoustic stimulus and to communicate thebrainstem response to the transducer controller; wherein the acousticstimulus comprises a signal having a transient peak element and asustain element; wherein the one or more data processing routines isconfigured to process the extracted brainstem response data stored inthe database to provide a clinical review of the response characteristicwith respect to normative data stored in the database; and wherein theone or more data processing routines is configured to generate acomposite score based on combination of one or more intermediate scoresand one or more group predictors to determine the presence of auditoryprocessing disability.
 2. The system of claim 1, the brainstem responsedata comprising an onset response portion including a data peak portionhaving a peak magnitude and a data trough portion having a troughmagnitude.
 3. The system of claim 1, the brainstem response datacomprising a frequency following portion including a data trough portionhaving a trough magnitude.
 4. The system of claim 1, wherein thecontroller is configured to compare a characteristic of a peak dataportion of the brainstem response data to the normative data.
 5. Thesystem of claim 1, wherein the controller is configured to compare acharacteristic of a trough data portion of the brainstem response datato the normative data.
 6. The system of claim 1, wherein the centralauditory processing disorder comprises a preconscious, disorderedprocessing of sound stimulus.
 7. The system of claim 1, wherein thecentral auditory processing disorder is associated with a learningdisability.
 8. The system of claim 1, the controller is configured torecommend a remedial measure in view of the central processing disorder.9. The system of claim 8, wherein the remedial measure comprises atleast one of a hearing assistive appliance or an auditory trainingregimen.